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. 2021 Aug 3;23:82. doi: 10.1186/s13058-021-01461-4

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 5 — FGFR2 and FGFR4 alterations in breast cancer patients. a Frequency of FGFR2 and FGFR4 alterations in different breast cancer subtypes. Data were extracted from the METABRIC dataset in cBioPortal. Only patients with FGFR alterations are displayed for brevity. b Immunohistochemical staining for FGFR4 on breast cancer specimens. PDX HCI-009 and ELX11-26 were used as positive and negative controls, respectively. A cohort of 12 luminal breast cancer and 13 TNBC samples were stained for FGFR4 expression. The frequency of positive and negative staining in these cohorts is represented in the bar graphs. c Association of FGFR4 alterations with patient prognosis. Kaplan–Meier plots using data from the METABRIC dataset indicating that patients with FGFR4 overexpression (left panel) or amplification (right panel) exhibit worse overall survival compared to those without FGFR4 alteration. A Logrank test was used where a p value of < 0.05 was considered significant. Survival data for the different patient groups were extracted and downloaded from cBioPortal and survival analysis performed using an in-house Matlab script